# -*- coding: utf-8 -*-
from datetime import timedelta
# 引入依赖
from jms.dm.dm_traffic_flow_network_new_dt import jms_dm__dm_traffic_flow_network_new_dt
from utils.operators.cluster_for_spark_sql_operator import SparkSqlOperator
from jms.time_sensor.time_after_05_45 import time_after_05_45
jms_dm__dm_agent_flow_direction_dt = SparkSqlOperator(
    task_id='jms_dm__dm_agent_flow_direction_dt',
    email=['houwenlong@jtexpress.com','yl_bigdata@yl-scm.com'],
    pool_slots=3,
    sla=timedelta(hours=7),
    master='yarn',
    name='jms_dm__dm_agent_flow_direction_dt_{{ execution_date | date_add(1) |cst_ds }}',
    sql='jms/dm/dm_agent_flow_direction_dt/execute.hql',
    driver_memory='1G' , 
    executor_memory='1G' , 
    executor_cores=1 , 
    num_executors=2 , 
    yarn_queue='pro',
    conf={'spark.dynamicAllocation.enabled': 'true',  # 动态资源开启
          'spark.shuffle.service.enabled': 'true',  # 动态资源 Shuffle 服务开启
        'spark.dynamicAllocation.maxExecutors'             : 2 , 
          'spark.dynamicAllocation.cachedExecutorIdleTimeout': 60,  # 动态资源自动释放闲置 Executor 的超时时间(s)
          'spark.sql.sources.partitionOverwriteMode': 'dynamic',  # 允许删改已存在的分区
        'spark.executor.memoryOverhead'             : '2G' , 
          },
    # hiveconf={'hive.exec.dynamic.partition': 'true',  # 开启动态分区
    #           'hive.exec.dynamic.partition.mode': 'nonstrict',  # 动态分区模式非严格
    #           },
    execution_timeout=timedelta(minutes=60),
)

jms_dm__dm_agent_flow_direction_dt << [jms_dm__dm_traffic_flow_network_new_dt
                                   
,time_after_05_45
]
